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Dive into the research topics where Max Neunhöffer is active.

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Featured researches published by Max Neunhöffer.


international symposium on symbolic and algebraic computation | 2006

A data structure for a uniform approach to computations with finite groups

Max Neunhöffer; Ákos Seress

We describe a recursive data structure for the uniform handling of permutation groups and matrix groups. This data structure allows the switching between permutation and matrix representations of segments of the input group, and has wide-ranging applications. It provides a framework to process theoretical algorithms which were considered too complicated for implementation such as the asymptotically fastest algorithms for the basic handling of large-base permutation groups and for Sylow subgroup computations in arbitrary permutation groups. It also facilitates the basic handling of matrix groups. The data structure is general enough for the easy incorporation of any matrix group or permutation group algorithm code; in particular, the library functions of the GAP computer algebra system dealing with permutation groups and matrix groups work with a minimal modification.


Journal of Symbolic Computation | 2010

Computing automorphisms of semigroups

João Araújo; Paul von Bünau; James D. Mitchell; Max Neunhöffer

In this paper an algorithm is presented that can be used to calculate the automorphism group of a finite transformation semigroup. The general algorithm employs a special method to compute the automorphism group of a finite simple semigroup. As applications of the algorithm all the automorphism groups of semigroups of order at most 7 and of the multiplicative semigroups of some group rings are found. We also consider which groups occur as the automorphism groups of semigroups of several distinguished types.


Lms Journal of Computation and Mathematics | 2008

COMPUTING MINIMAL POLYNOMIALS OF MATRICES

Max Neunhöffer; Cheryl E. Praeger

We present and analyse a Monte-Carlo algorithm to compute the minimal polynomial of an


Lms Journal of Computation and Mathematics | 2012

Condensation of homomorphism spaces

Klaus Lux; Max Neunhöffer; Felix Noeske

n\times n


parallel symbolic computation | 2010

Parallelising the computational algebra system GAP

Reimer Behrends; Alexander Konovalov; Steve Linton; Frank Lübeck; Max Neunhöffer

matrix over a finite field that requires


Lms Journal of Computation and Mathematics | 2005

Generalised Sifting in Black-Box Groups

Sophie Ambrose; Max Neunhöffer; Cheryl E. Praeger; Csaba Schneider

O(n^3)


Designs, Codes and Cryptography | 2014

Sporadic neighbour-transitive codes in Johnson graphs

Max Neunhöffer; Cheryl E. Praeger

field operations and O(n) random vectors, and is well suited for successful practical implementation. The algorithm, and its complexity analysis, use standard algorithms for polynomial and matrix operations. We compare features of the algorithm with several other algorithms in the literature. In addition we present a deterministic verification procedure which is similarly efficient in most cases but has a worst-case complexity of


Representation Theory of The American Mathematical Society | 2008

Formulas for primitive idempotents in Frobenius algebras and an application to decomposition maps

Max Neunhöffer; Sarah Scherotzke

O(n^4)


international congress on mathematical software | 2010

Towards high-performance computational algebra with GAP

Reimer Behrends; Alexander Konovalov; Steve Linton; Frank Lübeck; Max Neunhöffer

. Finally, we report the results of practical experiments with an implementation of our algorithms in comparison with the current algorithms in the {\sf GAP} library.


parallel computing | 2013

Space exploration using parallel orbits : a study in parallel symbolic computing

Vladimir Janjic; Christopher Brown; Max Neunhöffer; Kevin Hammond; Steve Linton; Hans-Wolfgang Loidl

We present an ecient algorithm for the condensation of homomorphism spaces. This provides an improvement over the known tensor condensation method which is essentially due to a better choice of bases. We explain the theory behind this approach and describe the implementation in detail. Finally, we give timings to compare with previous methods.

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Cheryl E. Praeger

University of Western Australia

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Steve Linton

University of St Andrews

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